> For the complete documentation index, see [llms.txt](https://docs.zingg.ai/latest/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.zingg.ai/latest/zingg-concepts/zingg-configuration/field-definition/match-types/numeric_with_units-match.md). # NUMERIC\_WITH\_UNITS Match Extracts product codes or numbers with units and compares how many are the same across both values. Built for product specification fields. ### What `NUMERIC_WITH_UNITS` does `NUMERIC_WITH_UNITS` extracts contiguous alphanumeric tokens that contain a digit - the number and any unit letters glued directly to it, like "16gb" or "2.4GHz" - and compares how many of those tokens are shared across two records. **The unit only glues to the number when there's no space between them.** "16gb" extracts as the single token `16gb`, but "16 GB" (with a space) extracts as just `16` - the unit is dropped, not glued. So a number and its unit written with a space in one record and without a space in the other extract to *different* tokens and won't match: `{16gb}` vs `{16}` share nothing. There is no unit-conversion logic either - "500ml" and "0.5L" extract to `{500ml}` and `{0.5L}`, which share nothing, regardless of them being the same physical quantity. **Null handling is a third, distinct behavior**, different from both `NUMERIC` and every other match type: if *both* sides extract zero tokens (both null/blank, or both have no digit anywhere), it scores a match (1.0). But if only *one* side extracts zero tokens - including one side being null while the other has real tokens - it scores a non-match (0.0), the same as `NUMERIC`. ### What `NUMERIC_WITH_UNITS` matches and what it does not
Value AValue BScoreNotes
16gb RAM16 GB Memory0.0 (No){16gb} vs {16} - the space in "16 GB" stops the unit from gluing to the number, so they extract as different tokens
16gb RAM32gb RAM0.0 (No){16gb} vs {32gb} share nothing
500ml0.5L0.0 (No){500ml} vs {0.5L} share nothing; there is no unit conversion
2.4GHz Dual Core2.4GHz Processor1.0 (Yes)2.4GHz extracted from both - same glued format on both sides
16gb160.0 (No){16gb} vs {16} share nothing
[null][null]1.0 (Yes)Both sides extract zero tokens - the auto-match case unique to this match type
[null]16gb RAM0.0 (No)Only one side extracts zero tokens - falls back to a non-match, same as NUMERIC
### When to use `NUMERIC_WITH_UNITS`
Product specification fields Any field containing product specifications with units - storage capacity, screen size, processor speed, volume, weight. E-commerce product catalogs, medical device specifications - as long as the number and unit are written glued together (no space) consistently in your data. "16GB RAM, 512GB SSD" and "8GB RAM, 512GB SSD" share the token "512GB" and score a partial match on that overlap. `NUMERIC_WITH_UNITS` works here because the unit is glued to the number the same way in both records.
### When not to use `NUMERIC_WITH_UNITS`
Address fields with numbers Use `NUMERIC` for address fields. "42 Main Street" does not have units. `NUMERIC_WITH_UNITS` is built for product specification patterns.
When your data mixes glued and spaced number-unit formatting "16GB RAM" and "16 GB RAM" do not match - the space in "16 GB" prevents the unit from gluing to the number, so they extract to different tokens (`16GB` vs `16`) with nothing in common. `NUMERIC_WITH_UNITS` does not normalise this, and there is no unit-conversion logic ("500ml" vs "0.5L" also won't match). If your source systems format this inconsistently, normalise the spacing (and units, if needed) upstream before matching.
{% hint style="success" icon="right-long" %} **Related types**: * `NUMERIC` - for numbers without unit * `TEXT` - for the descriptive parts of product fields **Read more**: [Match types](/latest/zingg-concepts/zingg-configuration/field-definition/match-types.md) {% endhint %} --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.zingg.ai/latest/zingg-concepts/zingg-configuration/field-definition/match-types/numeric_with_units-match.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.